A vision-based walking motion parameters capturing system

The markerless vision-based human motion parameters capturing has been widely applied for human-machine interface. However, it faces two problems: the high-dimensional parameter estimation and the self-occlusion. Here, we propose a 3-D human model with structural, kinematic, and temporal constraints to track a walking human object in any viewing direction. Our method modifies the Annealed Particle Filter (APF) by applying the pre-trained spatial correlation map and the temporal constraint to estimate the motion parameters of a walking human object. In the experiments, we demonstrate that the proposed method requires less computation time and generates more accurate results.